Abstract

We address the question to what extent the success of scientific articles is due to social influence. Analyzing a data set of over 100000 publications from the field of Computer Science, we study how centrality in the coauthorship network differs between authors who have highly cited papers and those who do not. We further show that a machine learning classifier, based only on coauthorship network centrality measures at time of publication, is able to predict with high precision whether an article will be highly cited five years after publication. By this we provide quantitative insight into the social dimension of scientific publishing - challenging the perception of citations as an objective, socially unbiased measure of scientific success.

Abstract

Among species providing uniparental care, the caring parent faces time constraints and may have to compromise offspring care/protection for self-maintenance. In most mammalian species females raise their offspring without receiving help from males. Communal nesting, when multiple females share a single nest where they rear their pups together, may have evolved as a mutually beneficial cooperative behaviour to reduce mothers' nest attendance without increasing the time their offspring are left alone. We tested this hypothesis using data collected in a free-living house mouse population where reproduction occurred in nest boxes and was closely monitored. Individuals were fitted with transponders allowing automatic recording of their location, and a genetic parentage analysis confirmed maternal identity. Compared to mothers raising their pups solitarily, communally nesting mothers spent less time inside their nest. Their pups, however, were left alone for a similar amount of time as solitarily raised pups. The time communal litters were left alone did not covary with the kinship of communally nesting females. These results indicate that communally nesting mothers can allocate more time to foraging or territorial defence without impairing the amount of maternal attention received by their offspring. Nevertheless, communally nesting mothers showed some overlap in their stays at the nest. Offspring may benefit from more regular meals while mothers may gain information on the partner's contribution to combined maternal care which could potentially prevent cheating.

Remarks

Featured Article in the June 2014 issue of Animal Behaviour (DOI: 10.1016/j.anbehav.2014.04.039).

Abstract

From the synchronised beauty of fish schools to the rigorous hierarchy of ant colonies, animals often display awe-inspiring collective behaviour. In recent years, principles of statistical physics have helped to unveil some simple mechanisms behind the emergence of such collective dynamics. Among the most elementary tools used to explain group behaviour are random processes, a typical example being the so-called “random walk”. In this paper, we have developed a framework based on such random assumptions to study the spatial and social structure of a population of wild house mice. We introduce the concept of perceptual landscape to describe the spatial behaviour of animals, whilst including all sensory and social constraints they are subject to: the perceptual landscape effectively maps the environment of animals as they perceive it. By applying our assumptions to a multi-agent model, we are able to reveal that much of the high-level social behaviour observed in the mouse population can indeed be explained through the many interactions of randomly moving individuals. This raises the question of how much of what we often regard as complex natural phenomena may, in fact, be the result of exceedingly simple forces.

Abstract

We investigate to what extent the interaction dynamics of a population of wild house mouse (Mus musculus domesticus) in their environment can be explained by a simple stochastic model. We use a Markov chain model to describe the transitions of mice in a discrete space of nestboxes, and implement a multi-agent simulation of the model. We find that some important features of our behavioural dataset can be reproduced using this simplified stochastic representation, and discuss the improvements that could be made to our model in order to increase the accuracy of its predictions. Our findings have implications for the understanding of the complexity underlying social behaviour in the animal kingdom and the cognitive requirements of such behaviour.